Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.

Journal: Journal of digital imaging
PMID: 35794502

Abstract

Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were > 0.99, with mean error of < 1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of < 1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow.

Authors

  • Nathan Larson
    Computer Science Department, Brigham Young University, Campus Dr, Provo, UT, 3361 TMCB84604, USA.
  • Chantal Nguyen
    Department of Orthopedic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Bao Do
    Department of Radiology, Stanford University Medical Center, Stanford, CA, USA.
  • Aryan Kaul
    University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Anna Larson
    Computer Science Department, Brigham Young University, Campus Dr, Provo, UT, 3361 TMCB84604, USA.
  • Shannon Wang
    University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Erin Wang
    Harvey Mudd College, Claremont, CA, 91711, USA.
  • Eric Bultman
    Department of Radiology, Palo Alto VA Medical Center, 3801 Miranda Ave, Palo Alto, CA, 94304, USA.
  • Kate Stevens
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Jason Pai
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Audrey Ha
    Menlo-Atherton High School, Atherton, CA, 94025, USA.
  • Robert Boutin
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Michael Fredericson
    Department of Orthopedic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Long Do
    , San Jose, CA, USA.
  • Charles Fang
    Department of Radiology, Palo Alto VA Medical Center, 3801 Miranda Ave, Palo Alto, CA, 94304, USA. Charles.Fang@va.gov.

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